Overview

Dataset statistics

Number of variables10
Number of observations1235
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory138.4 KiB
Average record size in memory114.8 B

Variable types

Numeric10

Alerts

prev close is highly overall correlated with open and 4 other fieldsHigh correlation
open is highly overall correlated with prev close and 4 other fieldsHigh correlation
high is highly overall correlated with prev close and 4 other fieldsHigh correlation
low is highly overall correlated with prev close and 4 other fieldsHigh correlation
last is highly overall correlated with prev close and 4 other fieldsHigh correlation
close is highly overall correlated with prev close and 4 other fieldsHigh correlation
compound is highly overall correlated with positiveHigh correlation
negative is highly overall correlated with neutralHigh correlation
neutral is highly overall correlated with negative and 1 other fieldsHigh correlation
positive is highly overall correlated with compound and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-03-05 16:25:33.292142
Analysis finished2023-03-05 16:26:06.146792
Duration32.85 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

prev close
Real number (ℝ)

Distinct1195
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1574.5372
Minimum767.7
Maximum2495
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.6 KiB
2023-03-05T16:26:06.442259image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum767.7
5-th percentile986.355
Q11190.95
median1428.6
Q31980.125
95-th percentile2295.345
Maximum2495
Range1727.3
Interquartile range (IQR)789.175

Descriptive statistics

Standard deviation452.57904
Coefficient of variation (CV)0.28743623
Kurtosis-1.3187247
Mean1574.5372
Median Absolute Deviation (MAD)374.1
Skewness0.26241596
Sum1944553.4
Variance204827.78
MonotonicityNot monotonic
2023-03-05T16:26:06.754793image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1053.8 2
 
0.2%
1198.7 2
 
0.2%
1229 2
 
0.2%
1250.3 2
 
0.2%
1050.65 2
 
0.2%
1923.4 2
 
0.2%
2122.65 2
 
0.2%
1028.75 2
 
0.2%
1027.55 2
 
0.2%
1430.9 2
 
0.2%
Other values (1185) 1215
98.4%
ValueCountFrequency (%)
767.7 1
0.1%
771.55 1
0.1%
813.85 1
0.1%
829.65 1
0.1%
830.65 1
0.1%
831.65 1
0.1%
836.65 1
0.1%
838.85 1
0.1%
852.4 1
0.1%
856.75 1
0.1%
ValueCountFrequency (%)
2495 1
0.1%
2489.65 1
0.1%
2485.55 1
0.1%
2483.8 1
0.1%
2472.4 1
0.1%
2467.9 1
0.1%
2462.3 1
0.1%
2457.1 1
0.1%
2452.3 1
0.1%
2448.4 1
0.1%

open
Real number (ℝ)

Distinct1098
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1575.1856
Minimum770.45
Maximum2499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.6 KiB
2023-03-05T16:26:07.047876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum770.45
5-th percentile984.97
Q11193.175
median1431
Q31980.825
95-th percentile2296.63
Maximum2499
Range1728.55
Interquartile range (IQR)787.65

Descriptive statistics

Standard deviation452.12897
Coefficient of variation (CV)0.28703219
Kurtosis-1.3112588
Mean1575.1856
Median Absolute Deviation (MAD)376
Skewness0.26824964
Sum1945354.2
Variance204420.61
MonotonicityNot monotonic
2023-03-05T16:26:07.349205image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1848 4
 
0.3%
1250 4
 
0.3%
1860 4
 
0.3%
1299 4
 
0.3%
1058 3
 
0.2%
1260 3
 
0.2%
1415 3
 
0.2%
1055 3
 
0.2%
1856 3
 
0.2%
1236 3
 
0.2%
Other values (1088) 1201
97.2%
ValueCountFrequency (%)
770.45 1
0.1%
794.6 1
0.1%
795.25 1
0.1%
836.45 1
0.1%
843 1
0.1%
847 1
0.1%
850 1
0.1%
853.8 1
0.1%
857 1
0.1%
859.9 1
0.1%
ValueCountFrequency (%)
2499 1
0.1%
2494.8 1
0.1%
2488 1
0.1%
2485.1 1
0.1%
2474.55 1
0.1%
2469.95 1
0.1%
2468.75 1
0.1%
2464 1
0.1%
2456.8 1
0.1%
2456 1
0.1%

high
Real number (ℝ)

Distinct1108
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1588.0871
Minimum810
Maximum2503.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.6 KiB
2023-03-05T16:26:07.654618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum810
5-th percentile994.685
Q11202.225
median1439.85
Q31998.95
95-th percentile2315.615
Maximum2503.3
Range1693.3
Interquartile range (IQR)796.725

Descriptive statistics

Standard deviation453.82864
Coefficient of variation (CV)0.28577062
Kurtosis-1.3201255
Mean1588.0871
Median Absolute Deviation (MAD)375.4
Skewness0.27725094
Sum1961287.6
Variance205960.44
MonotonicityNot monotonic
2023-03-05T16:26:07.946000image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1185 6
 
0.5%
1250 6
 
0.5%
1235 5
 
0.4%
1860 5
 
0.4%
2130 4
 
0.3%
1255 4
 
0.3%
1445 4
 
0.3%
1179.5 3
 
0.2%
2118 3
 
0.2%
1254 3
 
0.2%
Other values (1098) 1192
96.5%
ValueCountFrequency (%)
810 1
0.1%
838.75 1
0.1%
844 1
0.1%
863.85 1
0.1%
864 1
0.1%
867.3 1
0.1%
867.45 1
0.1%
870.3 1
0.1%
871.75 1
0.1%
873.6 1
0.1%
ValueCountFrequency (%)
2503.3 1
0.1%
2497.9 1
0.1%
2497.5 1
0.1%
2494.95 1
0.1%
2494.5 1
0.1%
2489 1
0.1%
2475 1
0.1%
2474.6 1
0.1%
2470 2
0.2%
2469.95 1
0.1%

low
Real number (ℝ)

Distinct1157
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1560.9098
Minimum738.75
Maximum2483
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.6 KiB
2023-03-05T16:26:08.240199image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum738.75
5-th percentile970.23
Q11181.025
median1415.55
Q31963.45
95-th percentile2280.145
Maximum2483
Range1744.25
Interquartile range (IQR)782.425

Descriptive statistics

Standard deviation451.00299
Coefficient of variation (CV)0.28893598
Kurtosis-1.3125718
Mean1560.9098
Median Absolute Deviation (MAD)374.65
Skewness0.25159508
Sum1927723.6
Variance203403.7
MonotonicityNot monotonic
2023-03-05T16:26:08.578366image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1181.1 3
 
0.2%
2280 3
 
0.2%
1235 3
 
0.2%
1425.05 3
 
0.2%
1250 3
 
0.2%
1785 3
 
0.2%
1230 3
 
0.2%
1106.2 2
 
0.2%
1085.2 2
 
0.2%
1788 2
 
0.2%
Other values (1147) 1208
97.8%
ValueCountFrequency (%)
738.75 1
0.1%
755.25 1
0.1%
765 1
0.1%
795 1
0.1%
810 1
0.1%
820 1
0.1%
824.55 1
0.1%
826.1 1
0.1%
828 1
0.1%
831.3 1
0.1%
ValueCountFrequency (%)
2483 1
0.1%
2472 1
0.1%
2467.2 1
0.1%
2460 1
0.1%
2456.15 1
0.1%
2450.05 1
0.1%
2441.05 1
0.1%
2440.35 1
0.1%
2437.05 1
0.1%
2432.1 1
0.1%

last
Real number (ℝ)

Distinct1131
Distinct (%)91.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1574.8941
Minimum774.8
Maximum2491.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.6 KiB
2023-03-05T16:26:09.404224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum774.8
5-th percentile984.33
Q11192
median1429.05
Q31978.1
95-th percentile2297.25
Maximum2491.15
Range1716.35
Interquartile range (IQR)786.1

Descriptive statistics

Standard deviation452.17052
Coefficient of variation (CV)0.2871117
Kurtosis-1.3165456
Mean1574.8941
Median Absolute Deviation (MAD)373.05
Skewness0.26336219
Sum1944994.2
Variance204458.18
MonotonicityNot monotonic
2023-03-05T16:26:09.717200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1275 4
 
0.3%
2115 4
 
0.3%
1440 4
 
0.3%
1950 3
 
0.2%
1245 3
 
0.2%
1554 3
 
0.2%
1244.9 3
 
0.2%
1185 3
 
0.2%
1229 3
 
0.2%
2117 3
 
0.2%
Other values (1121) 1202
97.3%
ValueCountFrequency (%)
774.8 1
0.1%
778.9 1
0.1%
811 1
0.1%
829.55 1
0.1%
831 1
0.1%
832.85 1
0.1%
838 1
0.1%
842.65 1
0.1%
857.95 1
0.1%
858 1
0.1%
ValueCountFrequency (%)
2491.15 1
0.1%
2488.3 1
0.1%
2486 1
0.1%
2482.85 1
0.1%
2469 1
0.1%
2465 1
0.1%
2460.6 1
0.1%
2455 1
0.1%
2454 1
0.1%
2447.05 1
0.1%

close
Real number (ℝ)

Distinct1195
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1574.8118
Minimum767.7
Maximum2495
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.6 KiB
2023-03-05T16:26:10.012175image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum767.7
5-th percentile986.355
Q11192.3
median1430.9
Q31980.125
95-th percentile2295.345
Maximum2495
Range1727.3
Interquartile range (IQR)787.825

Descriptive statistics

Standard deviation452.36521
Coefficient of variation (CV)0.28725032
Kurtosis-1.3176201
Mean1574.8118
Median Absolute Deviation (MAD)374.8
Skewness0.2617936
Sum1944892.6
Variance204634.28
MonotonicityNot monotonic
2023-03-05T16:26:10.322515image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1957.75 2
 
0.2%
1430.9 2
 
0.2%
1261.8 2
 
0.2%
1229 2
 
0.2%
1050.65 2
 
0.2%
1250.3 2
 
0.2%
1923.4 2
 
0.2%
1028.75 2
 
0.2%
2122.65 2
 
0.2%
1027.55 2
 
0.2%
Other values (1185) 1215
98.4%
ValueCountFrequency (%)
767.7 1
0.1%
771.55 1
0.1%
813.85 1
0.1%
829.65 1
0.1%
830.65 1
0.1%
831.65 1
0.1%
836.65 1
0.1%
838.85 1
0.1%
852.4 1
0.1%
856.75 1
0.1%
ValueCountFrequency (%)
2495 1
0.1%
2489.65 1
0.1%
2485.55 1
0.1%
2483.8 1
0.1%
2472.4 1
0.1%
2467.9 1
0.1%
2462.3 1
0.1%
2457.1 1
0.1%
2452.3 1
0.1%
2448.4 1
0.1%

compound
Real number (ℝ)

Distinct180
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.93908729
Minimum-0.9988
Maximum1
Zeros0
Zeros (%)0.0%
Negative37
Negative (%)3.0%
Memory size51.6 KiB
2023-03-05T16:26:10.640301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-0.9988
5-th percentile0.97147
Q10.9982
median0.9993
Q30.9997
95-th percentile0.9999
Maximum1
Range1.9988
Interquartile range (IQR)0.0015

Descriptive statistics

Standard deviation0.31443627
Coefficient of variation (CV)0.33483178
Kurtosis28.800067
Mean0.93908729
Median Absolute Deviation (MAD)0.0005
Skewness-5.4730467
Sum1159.7728
Variance0.098870168
MonotonicityNot monotonic
2023-03-05T16:26:10.979532image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9997 121
 
9.8%
0.9998 110
 
8.9%
0.9996 104
 
8.4%
0.9999 86
 
7.0%
0.9995 83
 
6.7%
0.9994 74
 
6.0%
0.9993 54
 
4.4%
0.9992 52
 
4.2%
0.9991 39
 
3.2%
0.9989 36
 
2.9%
Other values (170) 476
38.5%
ValueCountFrequency (%)
-0.9988 1
0.1%
-0.9982 1
0.1%
-0.998 1
0.1%
-0.9976 1
0.1%
-0.9972 1
0.1%
-0.9961 1
0.1%
-0.9958 1
0.1%
-0.9952 1
0.1%
-0.9947 1
0.1%
-0.9943 1
0.1%
ValueCountFrequency (%)
1 2
 
0.2%
0.9999 86
7.0%
0.9998 110
8.9%
0.9997 121
9.8%
0.9996 104
8.4%
0.9995 83
6.7%
0.9994 74
6.0%
0.9993 54
4.4%
0.9992 52
4.2%
0.9991 39
 
3.2%

negative
Real number (ℝ)

Distinct75
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.072774899
Minimum0.035
Maximum0.122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.6 KiB
2023-03-05T16:26:11.295699image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.035
5-th percentile0.052
Q10.064
median0.072
Q30.081
95-th percentile0.096
Maximum0.122
Range0.087
Interquartile range (IQR)0.017

Descriptive statistics

Standard deviation0.013014187
Coefficient of variation (CV)0.17882796
Kurtosis0.1812448
Mean0.072774899
Median Absolute Deviation (MAD)0.008
Skewness0.28465148
Sum89.877
Variance0.00016936906
MonotonicityNot monotonic
2023-03-05T16:26:11.615763image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.071 54
 
4.4%
0.068 52
 
4.2%
0.073 41
 
3.3%
0.079 40
 
3.2%
0.069 40
 
3.2%
0.072 39
 
3.2%
0.075 38
 
3.1%
0.074 37
 
3.0%
0.077 35
 
2.8%
0.078 35
 
2.8%
Other values (65) 824
66.7%
ValueCountFrequency (%)
0.035 1
 
0.1%
0.038 1
 
0.1%
0.04 2
 
0.2%
0.041 2
 
0.2%
0.042 2
 
0.2%
0.044 2
 
0.2%
0.046 2
 
0.2%
0.047 7
0.6%
0.048 7
0.6%
0.049 10
0.8%
ValueCountFrequency (%)
0.122 1
 
0.1%
0.12 1
 
0.1%
0.113 2
0.2%
0.112 1
 
0.1%
0.11 2
0.2%
0.109 1
 
0.1%
0.108 2
0.2%
0.107 2
0.2%
0.106 4
0.3%
0.105 4
0.3%

neutral
Real number (ℝ)

Distinct122
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.81262753
Minimum0.665
Maximum0.903
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.6 KiB
2023-03-05T16:26:11.897540image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.665
5-th percentile0.7767
Q10.796
median0.812
Q30.827
95-th percentile0.853
Maximum0.903
Range0.238
Interquartile range (IQR)0.031

Descriptive statistics

Standard deviation0.023530019
Coefficient of variation (CV)0.028955479
Kurtosis1.3482795
Mean0.81262753
Median Absolute Deviation (MAD)0.015
Skewness0.1708818
Sum1003.595
Variance0.0005536618
MonotonicityNot monotonic
2023-03-05T16:26:12.194539image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.804 31
 
2.5%
0.821 28
 
2.3%
0.815 27
 
2.2%
0.796 25
 
2.0%
0.807 25
 
2.0%
0.819 25
 
2.0%
0.813 24
 
1.9%
0.812 23
 
1.9%
0.795 23
 
1.9%
0.809 23
 
1.9%
Other values (112) 981
79.4%
ValueCountFrequency (%)
0.665 1
 
0.1%
0.742 1
 
0.1%
0.748 1
 
0.1%
0.757 1
 
0.1%
0.758 1
 
0.1%
0.76 1
 
0.1%
0.763 1
 
0.1%
0.764 1
 
0.1%
0.765 4
0.3%
0.766 3
0.2%
ValueCountFrequency (%)
0.903 1
0.1%
0.892 1
0.1%
0.889 1
0.1%
0.888 1
0.1%
0.887 2
0.2%
0.88 1
0.1%
0.879 1
0.1%
0.878 1
0.1%
0.877 2
0.2%
0.875 1
0.1%

positive
Real number (ℝ)

Distinct100
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11458381
Minimum0.056
Maximum0.281
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.6 KiB
2023-03-05T16:26:12.504462image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.056
5-th percentile0.086
Q10.104
median0.114
Q30.125
95-th percentile0.146
Maximum0.281
Range0.225
Interquartile range (IQR)0.021

Descriptive statistics

Standard deviation0.01821949
Coefficient of variation (CV)0.1590058
Kurtosis5.5202867
Mean0.11458381
Median Absolute Deviation (MAD)0.011
Skewness0.7226349
Sum141.511
Variance0.00033194982
MonotonicityNot monotonic
2023-03-05T16:26:12.802766image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.107 41
 
3.3%
0.123 38
 
3.1%
0.114 37
 
3.0%
0.116 36
 
2.9%
0.111 36
 
2.9%
0.109 35
 
2.8%
0.106 33
 
2.7%
0.113 33
 
2.7%
0.108 32
 
2.6%
0.11 29
 
2.3%
Other values (90) 885
71.7%
ValueCountFrequency (%)
0.056 1
 
0.1%
0.066 1
 
0.1%
0.067 1
 
0.1%
0.069 2
 
0.2%
0.07 2
 
0.2%
0.071 5
0.4%
0.073 3
0.2%
0.074 3
0.2%
0.075 1
 
0.1%
0.076 3
0.2%
ValueCountFrequency (%)
0.281 1
0.1%
0.175 2
0.2%
0.168 1
0.1%
0.166 1
0.1%
0.164 1
0.1%
0.163 1
0.1%
0.162 2
0.2%
0.161 2
0.2%
0.16 1
0.1%
0.159 2
0.2%

Interactions

2023-03-05T16:26:01.521483image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:34.111925image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:38.485296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:40.969035image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:43.537673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:46.943700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:50.944003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:53.441304image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:56.031182image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:58.581096image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:26:01.922466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:34.584069image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:38.742316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:41.220814image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:43.782464image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:47.311357image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:51.195983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:53.693655image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:56.277723image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:58.878104image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:26:02.252551image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:35.069077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:38.988683image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:41.500722image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:44.022244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:47.725863image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:51.460675image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:53.952923image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:56.519727image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:59.187560image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:26:02.595034image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:35.681779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:39.243590image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:41.778857image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:44.292236image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:48.636255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:51.711394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:54.219976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:56.760636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:59.440461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:26:02.969399image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:36.404060image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:39.495997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:42.029649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:44.659213image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:49.021458image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:51.957223image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:54.477532image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:57.009760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:59.679760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:26:03.368707image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:36.952691image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:39.745033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:42.283347image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:45.064231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:49.373670image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:52.219085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:54.737353image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:57.250776image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:59.916870image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:26:03.756331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:37.443309image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:40.009437image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:42.548918image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:45.463040image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:49.793046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:52.472559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:55.005623image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:57.507528image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:26:00.177263image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:26:04.108484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:37.764567image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:40.273538image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:42.808215image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:45.867992image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:50.216707image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:52.732780image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:55.277197image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:57.751377image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:26:00.526405image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:26:04.472848image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:37.996318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:40.513856image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:43.045280image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:46.180738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:50.463869image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:52.976013image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:55.524282image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:58.012683image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:26:00.839187image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:26:04.818374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:38.243724image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:40.744270image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:43.279004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:46.577464image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:50.695939image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:53.207815image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:55.772543image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:25:58.355261image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-05T16:26:01.178830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-03-05T16:26:13.041430image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
prev closeopenhighlowlastclosecompoundnegativeneutralpositive
prev close1.0000.9970.9960.9960.9950.995-0.2280.0670.020-0.069
open0.9971.0000.9990.9990.9980.998-0.2230.0610.020-0.065
high0.9960.9991.0000.9990.9990.999-0.2200.0600.019-0.063
low0.9960.9990.9991.0000.9990.999-0.2230.0570.023-0.067
last0.9950.9980.9990.9991.0001.000-0.2200.0570.022-0.064
close0.9950.9980.9990.9991.0001.000-0.2200.0570.021-0.064
compound-0.228-0.223-0.220-0.223-0.220-0.2201.000-0.380-0.3550.787
negative0.0670.0610.0600.0570.0570.057-0.3801.000-0.6310.119
neutral0.0200.0200.0190.0230.0220.021-0.355-0.6311.000-0.810
positive-0.069-0.065-0.063-0.067-0.064-0.0640.7870.119-0.8101.000

Missing values

2023-03-05T16:26:05.329154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-05T16:26:05.932613image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

prev closeopenhighlowlastclosecompoundnegativeneutralpositive
2016-01-011082.151082.401090.251076.151088.701088.750.99880.0870.7970.117
2016-01-041088.751084.001084.001068.101068.501070.500.99580.0960.7920.112
2016-01-051070.501070.201074.801061.351062.001062.40-0.99800.1060.8000.094
2016-01-061062.401056.651076.751056.651067.551067.100.99180.0900.8030.107
2016-01-071067.101060.101064.901049.701052.551056.200.99800.0740.8280.098
2016-01-081056.201061.951064.501057.251062.001062.35-0.99580.1000.8100.090
2016-01-111062.351052.051061.001045.301058.501058.600.99830.0800.8130.107
2016-01-121058.601063.901063.901043.501046.001046.950.99550.0460.8870.067
2016-01-131046.951052.001062.751037.001056.301060.150.99790.0820.8150.103
2016-01-141060.151050.151057.401039.501052.001049.750.99960.0600.8370.103
prev closeopenhighlowlastclosecompoundnegativeneutralpositive
2020-12-171410.701418.601445.001404.501440.001441.800.99970.0700.8090.122
2020-12-181441.801435.001439.701406.301408.951411.350.99920.0630.8440.093
2020-12-211411.351417.501423.851366.701369.051372.650.99930.0780.8130.109
2020-12-221372.651384.801384.801345.001373.001373.100.99920.0560.8550.089
2020-12-231373.101367.501380.951361.051378.001375.650.99980.0710.7980.130
2020-12-241375.651389.401404.001377.001395.901397.100.99980.0620.8190.119
2020-12-281397.101405.001421.001404.001415.801412.850.99970.0690.8190.112
2020-12-291412.851421.051434.751420.001427.951427.200.99890.0800.8110.110
2020-12-301427.201439.901439.901413.001432.051432.500.99930.0720.8150.113
2020-12-311432.501435.001444.001425.051438.451436.300.99980.0610.8020.136